TY - GEN
T1 - A Statistics-Based Dynamic Sequential Model Predictive Control for Induction Motor Drives
AU - Wang, Tianyi
AU - Wang, Yongdu
AU - Wang, Xingtao
AU - Han, Minghao
AU - Rodriguez, Jose
AU - Zhang, Zhenbin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Determining appropriate weighting factors is a key issue in finite control set model predictive control (FCS-MPC). The sequential model predictive control (SMPC) transforms the continuous weighting factors into fixed discrete optimization sequence and number of voltage vectors. In order to make these two parameters dynamic, this paper proposes a statistics-based dynamic sequential model predictive control scheme (Statistics-Based SMPC) for induction motor (IM) drives. This scheme focuses on the statistical characteristics of the cost function values, and uses the entropy weight method to dynamically determine the weight of the control targets, so that the optimization sequence can be dynamically changed with different working conditions. Another advantage of this scheme is that it is not limited by the number of control targets. Therefore, it has the potential to extend the cascade structure MPC without weighting factors to multiple control targets. Matlab/Simulink simulation verifies the effectiveness of the proposed method.
AB - Determining appropriate weighting factors is a key issue in finite control set model predictive control (FCS-MPC). The sequential model predictive control (SMPC) transforms the continuous weighting factors into fixed discrete optimization sequence and number of voltage vectors. In order to make these two parameters dynamic, this paper proposes a statistics-based dynamic sequential model predictive control scheme (Statistics-Based SMPC) for induction motor (IM) drives. This scheme focuses on the statistical characteristics of the cost function values, and uses the entropy weight method to dynamically determine the weight of the control targets, so that the optimization sequence can be dynamically changed with different working conditions. Another advantage of this scheme is that it is not limited by the number of control targets. Therefore, it has the potential to extend the cascade structure MPC without weighting factors to multiple control targets. Matlab/Simulink simulation verifies the effectiveness of the proposed method.
KW - Induction motor drives
KW - predictive control
KW - weighting factor elimination
UR - http://www.scopus.com/inward/record.url?scp=85125807123&partnerID=8YFLogxK
U2 - 10.1109/PRECEDE51386.2021.9681001
DO - 10.1109/PRECEDE51386.2021.9681001
M3 - Conference contribution
AN - SCOPUS:85125807123
T3 - 6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021
SP - 513
EP - 518
BT - 6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021
Y2 - 20 November 2021 through 22 November 2021
ER -